3_train.md

III. Train

Our approach contains two models: the core SAMS-GAN and the auxiliary WarpModule. We also compare against a baseline UNet-Mask model (based on the TOM model from CP-VTON).

The WarpModule is used to pre-warp the garment image to the shape of the user. It can be treated as a block box. You can just download the pre-warped cloths here (COMING SOON) and place them in ${PROJECT_ROOT}/warp-cloth. To warp cloths on your own data, we provide pretrained weights for the WarpModule that you can find here (COMING SOON).

View in Tensorboard

All training progress can be viewed in Tensorboard.

tensorboard --logdir experiments/

We can port forward a Tensorboard connection from a remote server like this:

ssh -N -L localhost:6006:localhost:6006 [email protected]

Common Train Options

Experiment Setup

• --name experiment name. Saves checkpoints and logs to experiments/{name}
• --gpu_ids
• --workers
• --keep_epochs let the optimizer handle the learning rate for this many epochs
• --decay_epochs linearly decay the learning rate for this many epochs (after completion of keep_epochs)

Data

• --vvt_dataroot path to FW-GAN VVT Dataset
• --warp_cloth_dir path to pre-warped cloths generated by the WarpModule (default: warp-cloth)
• --batch_size number of batches to run through model
• --person_inputs type of person representation, generally agnostic + (cocopose or densepose)
• --cloth_inputs type of cloth representation, (default: cloth)

Checkpointing and logging

• --display_count how often in steps to log to Tensorboard
• --save_count how often in steps to save a checkpoint
• --checkpoint resume training from this checkpoint (path to .ckpt file)

Choosing Architecture Design

• --self_attn flag to include attention layers in model architecture
• --flow_warp flag to add optical flow to model, requires n_frames_total > 1
• --activation select activation function (relu, gelu, swish, or sine)

... and more! For a complete list of options, run python train.py --help

ShineOn U-Net Mask Model

Instructions

python train.py \
--name train_shineon \
--model unet \
--batch 4 \
--person_inputs densepose agnostic \
--cloth_inputs cloth \
--val_check_interval  0.05 \
--self_attn \
--accumulated_batches 16 \
--activation gelu
--warp_cloth_dir /path/to/output/warp/cloth/directory

WarpModule (Optional)

Instructions

python train.py \
--name train_warp \
--model warp \
--workers 4 \
--batch 4

SAMS-GAN (experimental, not fully tested)

Instructions

A general train command:

python train.py \
--name "SAMS-GAN_train" \
--model sams \
--ngf_pow_outer 6 \
--ngf_pow_inner 10 \
--n_frames_total 5 \
--n_frames_now 1 \
--batch_size 4 \
--workers 8

Modules

Generator

The SAMS-GAN generator is an encoder-decoder architecture. The outer layers start with higher resolution (hxw) and fewer features. The inner layers have lower resolution and more features. Unlike other models, SAMS does NOT use --ngf for generator features.

Number of Layers

The number of features in the outer layers equals pow(ngf_power_base,ngf_pow_outer); by default, the outer layers have 2^6=64 features.

The number of features in the inner layers equals pow(ngf_power_base, ngf_pow_inner); by default, the inner layers have 2^10=1024 features.

Attention Layers

Self-Attentive Multispade (SAMs) layer indices can be chosen with:

• --attention_middle_indices for middle layers
• --attention_decoder_indices for decoder layers.

Supports negative index selection, e.g. use --attention_decoder_indices -1 -2 to put attention in the last two decoder layers.

Discriminators

SAMS-GAN has two discriminators: Multiscale that operates on the current frame at different image resolutions, and Temporal that operates at the past --n_frames_now at a single image resolution.

Discriminator size is uniformly adjusted with --ndf (default 64).

Progressive Training

We use progressive video frame training to speed up generation convergence. We start by generating a single image, then manually increase the number of frames to the max that fits on the GPU.

• --n_frames_total . Sets the size of the generation buffer, and how many previous frames are fed into the generator as input. Aim for the max that fits on GPU, 5 or more is ideal. Note that this effectively scales up the batch size; choosing between batch size and n_frames_total is a trade-off.
• --n_frames_now . The number of frames to actually train on right now. The rest of the frames are masked with 0s. You should progressively increase this value from 1 up to --n_frames_total.